



'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase:   Dict					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """roberta-prelayernorm"""







    def __init__( self					:    Dict		,     __snake_case					:    Optional[int]=5_02_65		,     __snake_case					:    int=7_68		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    List[Any]=12		,     __snake_case					:    str=30_72		,     __snake_case					:    Tuple="gelu"		,     __snake_case					:    Union[str, Any]=0.1		,     __snake_case					:    Tuple=0.1		,     __snake_case					:    List[str]=5_12		,     __snake_case					:    Dict=2		,     __snake_case					:    Any=0.02		,     __snake_case					:    Union[str, Any]=1e-1_2		,     __snake_case					:    Any=1		,     __snake_case					:    int=0		,     __snake_case					:    Dict=2		,     __snake_case					:    List[Any]="absolute"		,     __snake_case					:    Dict=True		,     __snake_case					:    Optional[Any]=None		,     **__snake_case					:    Dict		,     ):
      super().__init__(pad_token_id=__snake_case		,     bos_token_id=__snake_case		,     eos_token_id=__snake_case		,     **__snake_case      )

      a		:   Union[str, Any]	  =	vocab_size
      a		:   int	  =	hidden_size
      a		:   Optional[int]	  =	num_hidden_layers
      a		:   Any	  =	num_attention_heads
      a		:   List[str]	  =	hidden_act
      a		:   Optional[int]	  =	intermediate_size
      a		:   Union[str, Any]	  =	hidden_dropout_prob
      a		:   Any	  =	attention_probs_dropout_prob
      a		:   Tuple	  =	max_position_embeddings
      a		:   Optional[Any]	  =	type_vocab_size
      a		:   int	  =	initializer_range
      a		:   Union[str, Any]	  =	layer_norm_eps
      a		:   Union[str, Any]	  =	position_embedding_type
      a		:   Union[str, Any]	  =	use_cache
      a		:   Dict	  =	classifier_dropout

class 				a__(   lowerCamelCase__      ):







    @property
    def      lowercase_			( self					:    Dict      ):
      if self.task == "multiple-choice":
        a		:   Optional[int]	  =	{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
        a		:   List[str]	  =	{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]      )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model